Multi-voxel Pattern Analysis

Brain Mapping: An Encyclopedic Reference

Volumn 1, Issue , 2015, Pages 641-646

  Allefeld, C ^a   J D Haynes, ^a,b  

^a CHARITÉ UNIVERSITÄTSMEDIZIN BERLIN   (Germany)

^b HUMBOLDT UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84937406730     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1016/B978-0-12-397025-1.00345-6     Document Type: Chapter

References (34)

1. Allefeld, C., & Haynes, J.-D. Haynes. (2014). Searchlight-based multi-voxel pattern analysis of fMRI by cross-validated MANOVA. NeuroImage, 345-357. http://dx.doi. org/10.1016/j.neuroimage.2013.11.043.
2. Bishop, C. M. (2006). Pattern recognition and machine learning. New York: Springer.
3. Boser, B. E., Guyon, I., & Vapnik, V. (1992). A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop of computational learning theory (pp. 144-152). Pittsburgh: Association for Computing Machinery.
4. Chaimow, D., Yacoub, E., Ugurbil, K., & Shmuel, A. (2011). Modeling and analysis of mechanisms underlying fMRI-based decoding of information conveyed in cortical columns. NeuroImage, 56, 627-642.
5. Cortes, C., & Vapnik, V. (1995). Support vector networks. Machine Learning, 20, 273-297.
6. Edelman, S., Grill-Spector, K., Kushnir, T., & Malach, R. (1998). Towards direct visualization of the internal shape space by fMRI. Psychobiology, 26, 309-321.
7. Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, 179-188.
8. Fonov, V. S., Evans, A. C., McKinstry, R. C., Almli, C. R., & Collins, D. L. (2009). Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage, 47, S102.
9. Freeman, J., Brouwer, G. J., Heeger, D. J., & Merriam, E. P. (2011). Orientation decoding depends on maps, not columns. Journal of Neuroscience, 31, 4792-4804.
10. Friston, K. J., Frith, C. D., Frackowiak, R. S., & Turner, R. (1995). Characterizing dynamic brain responses with fMRI: A multivariate approach. NeuroImage, 2, 166-172.
11. Friston, K. J., Holmes, A. P., Poline, J. B., Grasby, P. J., Williams, S. C., Frackowiak, R. S., et al. (1995). Analysis of fMRI time-series revisited. NeuroImage, 2, 45-53.
12. Fujita, I., Tanaka, K., Ito, M., & Cheng, K. (1992). Columns for visual features of objects in monkey inferotemporal cortex. Nature, 360, 343-346.
13. Georgopoulos, A. P., Schwartz, A. B., & Kettner, R. E. (1986). Neuronal population coding of movement direction. Science, 233, 1416-1419.
14. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). New York: Springer.
15. Haynes, J.-D., & Rees, G. (2005). Predicting the orientation of invisible stimuli from activity in human primary visual cortex. Nature Neuroscience, 8, 686-691.
16. Haynes, J.-D., & Rees, G. (2006). Decoding mental states from brain activity in humans. Nature Reviews Neuroscience, 7, 523-534.
17. Jolliffe, I. T. (2002). Principal component analysis (2nd ed.). New York: Springer.
18. Kamitani, Y., & Sawahata, Y. (2010). Spatial smoothing hurts localization but not information: Pitfalls for brain mappers. NeuroImage, 49, 1949-1952.
19. Kamitani, Y., & Tong, F. (2005). Decoding the visual and subjective contents of the human brain. Nature Neuroscience, 8, 679-685.
20. Kriegeskorte, N., Cusack, R., & Bandettini, P. (2010). How does an fMRI voxel sample the neuronal activity pattern: Compact-kernel or complex spatiotemporal filter? NeuroImage, 49, 1965-1976.
21. Kriegeskorte, N., Goebel, R., & Bandettini, P. (2006). Information-based functional brain mapping. Proceedings of the National Academy of Sciences of the United States of America, 103, 3863-3868.
22. Kriegeskorte, N., Mur, M., & Bandettini, P. (2008). Representational similarity analysis-Connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2. http://dx.doi.org/10.3389/neuro.06.004.2008.
23. Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22, 79-86.
24. Mahalanobis, P. C. (1936). On the generalized distance in statistics. Proceedings of the National Institute of Sciences of India, 2, 49-55.
25. McIntosh, A. R., Bookstein, F. L., Haxby, J. V., & Grady, C. L. (1996). Spatial pattern analysis of functional brain images using partial least squares. NeuroImage, 3, 143-157.
26. Nevado, A., Young, M. P., & Panzeri, S. (2004). Functional imaging and neural information coding. NeuroImage, 21, 1083-1095.
27. Op de Beeck, H. P. (2010). Against hyperacuity in brain reading: Spatial smoothing does not hurt multivariate fMRI analyses? NeuroImage, 49, 1943-1948.
28. Platt, J. C. (2000). Probabilities for SV machines. In A. J. Smola, et al. (Eds.), Advances in large margin classifiers (pp. 61-74). Cambridge: The MIT Press.
29. Schäfer, J., & Strimmer, K. (2005). A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Statistical Applications in Genetics and Molecular Biology, 4, 32.
30. Shmuel, A., Chaimow, D., Raddatz, G., Ugurbil, K., & Yacoub, E. (2010). Mechanisms underlying decoding at 7 T: Ocular dominance columns, broad structures, and macroscopic blood vessels in V1 convey information on the stimulated eye. NeuroImage, 49, 1957-1964.
31. Swisher, J. D., Gatenby, J. C., Gore, J. C., Wolfe, B. A., Moon, C. H., Kim, S. G., et al. (2010). Multiscale pattern analysis of orientation-selective activity in the primary visual cortex. Journal of Neuroscience, 30, 325-330.
32. Timm, N. H. (2002). Applied multivariate analysis. New York: Springer.
33. Triantafyllou, C., Hoge, R. D., & Wald, L. L. (2006). Effect of spatial smoothing on physiological noise in high-resolution fMRI. NeuroImage, 32, 551-557.
34. Vapnik, V. (1982). Estimation of dependences based on empirical data, Addendum 1. New York: Springer.